I am very pleased to announce that the new SMOS-IC soil moisture product is now available as a science product on the CATDS:

The SMOS INRA-CESBIO (SMOS-IC) algorithm was designed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère) to perform global retrievals of SM and L-VOD using some simplifications with respect to the Level 2 ESA algorithm. The SMOS-IC algorithm and dataset is described in Fernandez-Moran et al. (2017). SMOS -IC was designed on the same basis as the level 2 SM algorithm, i.e., L-MEB (Wigneron et al, 2007). However, one of the main goals of the SMOS-IC product is to be as independent as possible from auxiliary data so as to be more robust and less impacted by potential uncertainties in the afore mentioned auxiliary data sets. It also differs from the SMOS Level 2 product in the treatment of retrievals over regions with a heterogeneous land cover (partially forested areas). Specifically, SMOS-IC does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous.

The current version is 105 and it is provided in the 25km EASEv2 grid, as netcdf format. SMOS IC is a scientific product delivered by the CATDS, i.e. meaning it is not updated on a daily basis as an operational product for the time being.

We re looking forward to receiving your feed back as we intend to make it an operational product soon.

We will soon deploy the companion SMOS-IC VOD (vegetation Optical Depth) product as well as a corresponding Level 3 for both SM and VOD obtained with SMOS-IC

Also Note that very soon we will deploy another new product (yes), i.e., SMOS brightness temperature in polar projection

After the illustrations of some striking results over oceans, we can only marvel, especially as many other aspects were not covered. Eight years ago we did not have any of such applications and science return. Those span from rainfall estimates over oceans to wind speed retrievals for strong winds (tropical storms, hurricanes and the like) where wind scatterometers do saturate for lower wind speeds. SMOS, Aquarius and now SMAP do show that L band measurements bring forward many new science obviously but also many very practical and societal applications which are not fulfilled without them.

This also applies for land of course where new applications blossomed at an unprecedented rate.

It exemplifies, to me at least, how real measurements can never be replaced by proxies. The first radar for EO flew in 1977 (yes 40 years ago!), the scatterometers with Envisat have been available since 1991 but we have yet to see a real soil moisture map from these. Intrinsically active systems are more sensitive to structure that to content and radar soil moisture are at best validated only over small areas where all is known, and similarly to scatterometers, rely on change detection (yes I know I am partial but I can claim that I started fiddling with radars 40 years ago and was one of the pro SCAT over land (convincing ESA to make the sigma nought triplets available over land which was not originally planned incidentally), but to realise soon that it was no game for absolute retrievals). Which means that they have to be scaled and that the validity at point (xi,yi) and no relationship with the validity at point (xj, yj) etc … but this is another story…To make a long story short a nicely coloured map has never make an accurate map.

With L band radiometry no such issues and if properly done, you have access to the soil moisture per se. As a direct consequence, and in opposition to active systems, a few months only after the release of the data the first applications emerged. We saw the first use in food security (W Crow , USDA), the first drought indices really related to what was happening (A Al Bitar detecting the drought in California in 2011 when the official drought index was to detect it only a couple of years later) or monitoring the Mississippi floods and levees destruction in 2011, the making of a flood risk forecasting tool demonstrator, the Spanish BEC fire risk analysis tool, etc… etc.. etc…

There isn’t enough room in a blog to document all this so I am giving only three samples.

1) high resolution soil moisture map

One of the main limitations of passive microwave is the spatial resolution. Olivier Merlin and his team developed an approach which -in many cases enables to monitor soil moisture with a 1 km resolution as shown in the example below.

It can be successfully applied at 100 m in some cases (irrigation optimisation) as shown Catalonia (MJ Escorihuela). Other approaches rely on the use of active systems as originally planned for SMAP (N. Das) and done with SMOS (S. Tomer) or SMAP with Sentinel 1. Ideally the two approaches should be merged to my feeling.

Uses for such derived high resolution products are obvious, for irrigation and hydrology as already mentioned, but also for pest control (Locusts in Africa) or epidemiology (dengue, zika and malaria to name but a few). Moreover it can be used to derive high resolution root zone soil moisture and other passive L band products.

2) Rainfall estimates over land

It is known that rainfall mission (TRMM to GPM) are very useful tool for estimating rainfall distribution over land. It is also well known that estimating rainfall with one instantaneous measurement every so often is somewhat difficult. Sometimes and in some areas/context, the cumulated errors amount to several folds. The idea is thus to assimilate soil moisture estimates so as to « correct » the GPM rainfall estimates. Pellarin, Brocca and Crow and others demonstrated the efficiency of this approach.

Soil moisture is a driven of crop yield in many areas. First shown by B. Hornebuckle with SMOS, Gibon and Pellarin went one step further by identifying which soil moisture (30 cm deep) and which period (grain filling and to a lesser extent reproductive) of vegetation growth where the drivers for millet in Western Africa. They then compared their local estimates with FAO global maps and found excellent correlation. It is interesting to see that departures are linked to local events

Examples like this can be multiplied, I just picked some low hanging fruit. One can say that such applications an science results could be expected and were delivered in record time. This blog is probably already way too long and I did not cover very interesting and promising results on evapotranspiration for instance, or hydrology, not to mention cryosphere … I keep the latter for tomorrow!

The Bay of Bengal receives large amounts of freshwater from the Ganges-Brahmaputra river and monsoonal rainfall. The associated very low surface salinities induce a very stable stratification that inhibits vertical mixing of heat and nutrients. This has strong consequences for the climatological rainfall, intensification of tropical cyclones and ocean productivity in this region.

Available climatologies based on in situ data (e.g. World Ocean Atlas, top row) do not resolve the very strong horizontal gradients in this region. SMOS data (middle row) reveal that the narrow, coastal-trapped East-Indian Coastal Current transport the freshwater plume of Ganges-Brahmaputra along the Indian coast from October to December, resulting in large horizontal gradients (typically ~5 pss between coastal and offshore waters). The 8 years-long time series reveals a strong inter-annual variability of the freshwater plume southward extent, which can be related to Indian Ocean climate variability.

Caption: World ocean atlas (derived from in situ data, top row) and SMOS (middle row) (SSS climatology (altimeter-derived surface current climatology are overlaid on both panels). (Bottom row) Latitude-time section of SMOS SSS along the east coast of India. The southward extent of the freshwater plume varies depending on Indian Ocean climate variability associated with the Indian Ocean Dipole (Akhil et al. in prep.). (SMOS CATDS CPDC L3Q SSS)

Today let’s have a look back on what was done over land… but remember: it is only a quick summary of part of the findings!!

Of course all the emphasis at the beginning was on the soil moisture retrievals over what as called « nominal surfaces », which meant land surface with moderate vegetation cover (fallow, crop land, savannah etc..) with all the cal val efforts related to it. For this in particular, several sites were dedicated to Cal Val (VAS in Spain, UDB in Germany, AACES/COSMOS/NAFE in Australia, and later HOBE in Denmark, with also sites in France, Poland, Finland, Tibet, etc…). We also relied heavily on the USDA so called « Watershed sites » and various sparse networks. Actually it is for SMOS that ESA and NASA decided to start the International Soil moisture Network.

Various pictures SMOSREX, AACES, VAS, Crolles, Mysore, Sodankylä …

Surprisingly enough we obtained good results almost immediately. But this was only the beginning as, in parallel, both level 1 and level 2 made significant progresses, leading to always improved retrievals. Actually with such fast progresses, it has always been a bit of a frustration to see people use not up to date products, as publications looking at SMOS data tended – for obvious reasons – to be a couple of version old (but generally failed to stipulate which version they were looking at!).

The most striking features of these always improved retrievals was, to me, the fact that the range of validity tended to regularly increase. Low to medium topography did not seem to a be a limitation, we managed to make sense in case of flooded areas (see for instance Mississipi floods) and we could get information in case of dense vegetation. The Tor Vergata University for instance related very quickly the vegetation depth to tree height and performed soil moisture retrievals under rainforest. No so accurate of course, but the tendencies are well depicted.

SMOS opacity vs tree height from ICESat for two season (Rahmoune et al)

The only trouble we had was that the vegetation optical depth was not as satisfactory as we would have expected. It remained noisy in spite of significant overall progresses. To address this problem and also to keep on improving our retrievals (parametrisations) INRA and CESBIO worked on a different approach, the so called SMOS-IC and, lo and behold, first results are rather amazing! We believe we have again struck gold. More about this in the near future!

To finish with the surface soil moisture and vegetation opacity retrievals, we were faced with the fact that the retrieval algorithm is not so fast and thus tests or re-processings are a lengthy and tedious. This was another motivation for SMOS-IC but we also wanted to go a step further and, as soon as enough data was acquired, we developed a global neural network retrieval scheme. It has since been implemented in ECMWF and delivers Soil moisture fields less than 3 hours of sensing, paving the way to many applications…. to be summarised soon: stay tuned!